Deep Learning-Based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia

    December 2021 in “ Acta dermato-venereologica ”
    Meng Gao, Yue Wang, Haipeng Xu, Congcong Xu, Xue Yang, Jin Nie, Ziye Zhang, Zhixuan Li, Wei Hou, Yiqun Jiang
    TLDR A deep learning model accurately predicts male hair loss types using scalp images.
    The study developed a deep learning framework using 2,910 trichoscopic images to predict basic and specific classifications in male androgenetic alopecia. A convolutional neural network-based framework was created to analyze hair density and diameter distribution, which were correlated with alopecia classifications. Additionally, a quantitative model using multiple ordinal logistic regression was developed for prediction. The study successfully established both a deep learning framework and a quantitative model for accurate prediction of alopecia classifications based on trichoscopic image analysis.
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